1 Set up working Environment

1.1 Load required libraries and set working directory

workingdir="/Users/elsa/Desktop/THESIS/sled_dog_metagenomics"
setwd(workingdir)
load("resources/data/data.Rdata")

2 Data pre-processing

read_counts_row <- column_to_rownames(read_counts, "genome")
nsamples <- ncol(read_counts_row) # define number of samples, by reading from the counts table
metagenomic_bases <- sum(sample_metadata$metagenomic_bases)
host_bases <- sum(sample_metadata$host_bases)
discarded_bases <- sum(round(((sample_metadata$metagenomic_bases+sample_metadata$host_bases)/(1-sample_metadata$bases_lost_fastp_percent))-(sample_metadata$metagenomic_bases+sample_metadata$host_bases))) #define amount of discarded bases from quality filtering
total_bases <- discarded_bases + host_bases + metagenomic_bases
singlem_bases <- sum(sample_metadata$metagenomic_bases * sample_metadata$singlem_fraction) 
nmags <- nrow(read_counts_row) # establish number of MAGs for analysis

sequencing_depth <- colSums(read_counts_row)
sequencing_depth_sum <- sum(sequencing_depth)
sequencing_depth_mean <- mean(sequencing_depth)
sequencing_depth_sd <- sd(sequencing_depth)

2.1 General statistics

Number of samples in total

[1] 58

Number of MAGs The number of metagenome-assembled genomes (MAG) or draft bacterial genomes reconstructed from the metagenomic data.

Amount of total data (GB): The amount of total DNA data sequenced in gigabases (GB, one billion nucleotide bases).

333.71

Amount of discarded data (GB): The amount of data discarded due to low quality or lack of informativeness during data preprocesing. Discarding 5-15% of the produced data is within the expected range, due to formation of adaptor dimers, inclusion of adaptors in sequencing reads due to short insert sizes, low sequencing quality, etc.

10.36577

Amount of discarded data (in % of the raw data):

3.11

Amount of host data (GB): The amount of data mapped against the host genome. The percentage refers to the amount of data mapped to the host genome respect to quality-filtered data. Note that this value can be very variable depending on the biological features of the sample (e.g., anal swabs contain more host DNA than faeces) and the employed reference genome (e.g., the chances for mapping to the genome are lower as the distance between) the study species and the employed reference genome differ).

6.49

Amount of host data (% of the quality-filtered data):

2.01

Estimated prokaryotic data: The amount and proportion of data belonging to prokayotic genomes respect to the total metagenomic fraction, as estimated from singleM analysis. Note that this is an estimation that relies on the genome sizes of genomes available in reference databases. If a given taxon is not properly represented, genome size estimations can be less accurate.

293.14

Estimated prokaryotic data (% of the metagenomic data):

92.51

Amount of metagenomic data (GB): The amount of data mapped against the host genome. The percentage refers to the amount of data mapped to the host genome respect to quality-filtered data. Note that this value can be very variable depending on the biological features of the sample (e.g., anal swabs contain more host DNA than faeces) and the employed reference genome (e.g., the chances for mapping to the genome are lower as the distance between) the study species and the employed reference genome differ).

316.85

Amount of metagenomic data (% of the quality-filtered data):

97.99

Total mapped sequencing depth (million reads): The amount of reads (and nucleotide bases) that were mapped to the entire MAG catalogue. Note that the amount of bases is only an approximation estimated by multiplying the exact number of mapped reads by 250 bp.

1860.26

Total mapped sequencing depth (GB):

266.02

Average mapped sequencing depth (million reads): This is the average number of reads (and nucleotide bases) mapped to each sample. Note that the amount of bases is only an approximation estimated by multiplying the exact number of mapped reads by 250 bp.

32.07

Average mapped sequencing depth (GB):

4.59

2.2 General MAG statistics

Number of MAGs without species-level annotation

Percentage of MAGs without species-level annotation

43.96396

Number of phyla

[1] 12

Number of genera

[1] 135

3 MAG catalogue

3.1 MAG tree

heatmap <- ehi_phylum_colors %>%
  dplyr::right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    arrange(match(genome, tree$tip.label)) %>%
  dplyr::select(genome,phylum) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome")

circular_tree <- force.ultrametric(tree,method="extend") %>%
    ggtree(., layout = 'circular', size = 0.3)

# Phylum ring
circular_tree <- gheatmap(circular_tree, heatmap, offset=0.65, width=0.1, colnames=FALSE) +
        scale_fill_manual(values=colors_alphabetic) +
        geom_tiplab2(size=1, hjust=-0.1) +
        theme(plot.margin = margin(0, 0, 0, 0), panel.margin = margin(0, 0, 0, 0))

# Flush color scale to enable a new color scheme in the next ring
circular_tree <- circular_tree + new_scale_fill()

# Add completeness ring
circular_tree <- circular_tree +
        new_scale_fill() +
        scale_fill_gradient(low = "#d1f4ba", high = "#f4baba") +
        geom_fruit(
                data=genome_metadata,
                geom=geom_bar,
                mapping = aes(x=completeness, y=genome, fill=contamination),
                offset = 0.55,
                orientation="y",
              stat="identity")

# Add genome-size ring
circular_tree <-  circular_tree +
        new_scale_fill() +
        scale_fill_manual(values = "#cccccc") +
        geom_fruit(
             data=genome_metadata,
             geom=geom_bar,
             mapping = aes(x=mag_size, y=genome),
                 offset = 0.05,
                 orientation="y",
         stat="identity")

#Plot circular tree
circular_tree

3.2 MAG quality

Overview of the taxonomy and genome characteristics of the MAGs.

Completeness: completeness of the MAG according to CheckM assessment.
Contamination: contamination or redundancy of the MAG according to CheckM assessment.
Size: size of the MAG in megabases (MB, one million nucleotide bases).

3.3 Sequencing depth assessment

3.4 Estimated vs. mapped prokaryotic fraction

3.5 Additional sequencing needed

3.6 Relative abundance

genome_counts %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  dplyr::left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  dplyr::left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors) +
    labs(y = "Relative abundance") +
  facet_grid(.~region,  scales="free_x")+
#    facet_nested(.~Individual+Sample_type,  scales="free_x") +
    guides(fill = guide_legend(ncol = 1)) +
    theme(axis.text.x=element_blank(),
          axis.title.x = element_blank(),
          axis.ticks.x=element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
   labs(fill="Phylum")

4 Alpha diversity calculations

Diversity estimations for each sample.

Richness: Number of MAGs per sample (after applying coverage filter).
Neutral diversity: Hill number of q=1 (Shannon diversity), a diversity metric that accounts for richness and eveness (relative abundances) of the MAGs.
Phylogenetic diversity: Phylogenetic Hill number of q=1, a diversity metric that accounts for richness and eveness (relative abundances), as well as phylogenetic relationships among MAGs.
Functional diversity: Functional Hill number of q=1, a diversity metric that accounts for richness and eveness (relative abundances), as well as functional dissimilarities among MAGs.

4.1 Alpha diversity means by sample type

#Calculate Hill numbers
richness <- genome_counts %>% 
            column_to_rownames(var="genome") %>% 
            dplyr::select(where(~!all(. == 0))) %>% 
            hilldiv(.,q=0) %>% 
            t() %>% 
            as.data.frame() %>%
            dplyr::rename(richness=1) %>%
            rownames_to_column(var="sample")

neutral <- genome_counts %>% 
            column_to_rownames(var="genome") %>% 
            dplyr::select(where(~!all(. == 0))) %>% 
            hilldiv(.,q=1) %>% 
            t() %>% 
            as.data.frame() %>%
            dplyr::rename(neutral=1) %>%
            rownames_to_column(var="sample")

phylogenetic <- genome_counts %>% 
            column_to_rownames(var="genome") %>% 
            dplyr::select(where(~!all(. == 0))) %>% 
            hilldiv(.,q=1,tree=tree) %>% 
            t() %>% 
            as.data.frame() %>%
            dplyr::rename(phylogenetic=1) %>%
            rownames_to_column(var="sample")

# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
    to.elements(., GIFT_db) %>%
    traits2dist(., method="gower")

genome_counts_filt <- genome_counts[genome_counts$genome %in% rownames(genome_gifts),] 
rownames(genome_counts_filt) <- NULL
functional <- genome_counts_filt %>% 
            column_to_rownames(var="genome") %>% 
            dplyr::select(where(~!all(. == 0))) %>% 
            hilldiv(.,q=1,dist=dist) %>% 
            t() %>% 
            as.data.frame() %>%
            dplyr::rename(functional=1) %>%
            rownames_to_column(var="sample") %>%
            mutate(functional = if_else(is.nan(functional), 1, functional))

# Merge all metrics
alpha_div <- richness %>%
      dplyr::full_join(neutral,by=join_by(sample==sample)) %>%
      dplyr::full_join(phylogenetic,by=join_by(sample==sample)) %>%
      dplyr::full_join(functional,by=join_by(sample==sample)) %>%
      dplyr::left_join(., sample_metadata, by = join_by(sample == sample))


richness_mean <- alpha_div %>%
  group_by(region) %>%
  dplyr::summarise_at(.vars = names(.)[2], .funs = c("Richness mean" = "mean", "Richness sd" = "sd")) %>%
  dplyr::rename("Region"="region")

neutral_mean <- alpha_div %>%
  group_by(region) %>%
  dplyr::summarise_at(.vars = names(.)[3], .funs = c("Neutral mean" = "mean", "Neutral sd" = "sd"))

phylogenetic_mean <- alpha_div %>%
  group_by(region) %>%
  dplyr::summarise_at(.vars = names(.)[4], .funs = c("Phylogenetic mean" = "mean", "Phylogenetic sd" = "sd"))

functional_mean <- alpha_div %>%
  group_by(region) %>%
  dplyr::summarise_at(.vars = names(.)[5], .funs = c("Functional mean" = "mean", "Functional sd" = "sd"))

cbind(richness_mean, neutral_mean[, 2:3], phylogenetic_mean[, 2:3], 
      functional_mean[, 2:3]) %>% 
  as.data.frame()%>%
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  knitr::kable(.,digits = c(3,3))

	Daneborg	Ittoqqortoormiit
Richness mean	233.103	232.138
Richness sd	48.115	44.915
Neutral mean	102.846	96.607
Neutral sd	20.952	37.606
Phylogenetic mean	5.129	5.936
Phylogenetic sd	0.812	1.382
Functional mean	1.423	1.453
Functional sd	0.034	0.058

4.2 Plot alpha diversities

alpha_colors <- c("#e5bd5b", "#6b7398")
group_n <- alpha_div %>%
  dplyr::select(region) %>%
  pull() %>%
  unique() %>%
  length()

# all alpha diversity plots follow the same structure, thus only the script for neutral alpha diversity is shown. 

plot1 <- alpha_div %>%
  ggplot(aes(x = region, y = richness, group = region, color = region, fill = region)) +
  geom_jitter(width = 0.05, size = 1.5, show.legend = FALSE) +
  geom_violin(alpha = 0.2, width = 0.3, show.legend = FALSE) +
  scale_color_manual(values = alpha_colors[c(1:group_n)]) +
  scale_fill_manual(values = paste0(alpha_colors[c(1:group_n)], "50")) +
  stat_compare_means(show.legend = FALSE) +
  theme(axis.text.x = element_text(vjust = 0.5),
    axis.title = element_text(size = 14, face = "bold"),
    axis.text = element_text(face = "bold", size = 12),
    axis.title.y = element_text(margin = margin(t = 0, r = 20, b = 0, l = 0)),
    axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)),
    panel.background = element_blank(),
    # panel.border = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.line = element_line(size = 0.5, linetype = "solid", colour = "black")) +
  guides(colour = guide_legend(override.aes = list(size = 6))) +
  labs(x = "Region", y = "Richness")

5 Beta diversity

beta_q0 <- genome_counts%>%
  column_to_rownames(., "genome")%>%
  hillpair(., q = 0)

beta_q1n <- genome_counts%>%
  column_to_rownames(., "genome")%>%
  hillpair(., q = 1)

beta_q1p <- genome_counts%>%
  column_to_rownames(., "genome")%>%
  hillpair(., q = 1, tree = tree)

beta_div_func <- genome_counts_filt%>%
  column_to_rownames(., "genome")%>%
  hillpair(.,q=1,dist=dist)

save(beta_q0,beta_q1n, beta_q1p, beta_div_func, file = "resources/data/beta_div.Rdata")

beta_metric <- beta_q1n$S

beta_q1n_nmds <- beta_metric %>%
  vegan::metaMDS(., trymax = 500, k = 2, verbosity = FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata, by = join_by(sample == sample))

group_n <- length(unique(beta_q1n_nmds$region))
beta_colors <- c("#e5bd5b", "#6b7398")

5.1 Neutral beta diversity

# all beta diversity plots follow the same format, thus only the script for neutral beta diversity is shown. 

q1n_nmds <- beta_q1n_nmds %>%
  group_by(region) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup()
ggplot(q1n_nmds, aes(x = NMDS1, y = NMDS2, color = region)) +
  scale_color_manual(values = beta_colors[c(1:group_n)]) +
  scale_shape_manual(values = 1:10) +
  geom_point(size = 4) +
  #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
  geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
  theme_classic() +
  theme(
    axis.text.x = element_text(size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 20, face = "bold"),
    axis.text = element_text(face = "bold", size = 18),
    panel.background = element_blank(),
    axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
    legend.text = element_text(size = 16),
    legend.title = element_text(size = 18),
    legend.position = "right", legend.box = "vertical"
  )

Homogeneity of variance


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.06203 0.062033 2.7774    999  0.104
Residuals 56 1.25076 0.022335                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                 Daneborg Ittoqqortoormiit
Daneborg                             0.106
Ittoqqortoormiit  0.10119

Permanova

	Df	SumOfSqs	R2	F	Pr(>F)
region	1	2.376	0.288	22.631	0.001
sex	2	0.200	0.024	0.951	0.440
Residual	54	5.670	0.688	NA	NA
Total	57	8.245	1.000	NA	NA

5.2 Phylogenetic beta diversity

Homogeneity of variance


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.02909 0.029089 2.7299    999  0.103
Residuals 56 0.59672 0.010656                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                 Daneborg Ittoqqortoormiit
Daneborg                             0.104
Ittoqqortoormiit  0.10408

Permanova

	Df	SumOfSqs	R2	F	Pr(>F)
region	1	0.139	0.102	6.326	0.001
sex	2	0.042	0.031	0.964	0.403
Residual	54	1.184	0.867	NA	NA
Total	57	1.365	1.000	NA	NA

5.3 Functional beta diversity

Homogeneity of variance


Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.001164 0.0011635 0.2775    999  0.627
Residuals 56 0.234777 0.0041924                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
                 Daneborg Ittoqqortoormiit
Daneborg                             0.632
Ittoqqortoormiit  0.60041

Permanova

	Df	SumOfSqs	R2	F	Pr(>F)
region	1	0.016	0.043	2.521	0.186
Residual	56	0.363	0.957	NA	NA
Total	57	0.379	1.000	NA	NA

6 Functional capacity analysis

6.1 Functional GIFT distillation:

#Aggregate bundle-level GIFTs into the compound level
GIFTs_elements <- to.elements(genome_gifts,GIFT_db)
GIFTs_elements_filtered <- GIFTs_elements[rownames(GIFTs_elements) %in% genome_counts_filt$genome,]
GIFTs_elements_filtered <- as.data.frame(GIFTs_elements_filtered) %>% 
  select_if(~ !is.numeric(.) || sum(.) != 0)

#Aggregate element-level GIFTs into the function level
GIFTs_functions <- to.functions(GIFTs_elements_filtered,GIFT_db)

#Aggregate function-level GIFTs into overall Biosynthesis, Degradation and Structural GIFTs
GIFTs_domains <- to.domains(GIFTs_functions,GIFT_db)

#Get community-weighed average GIFTs per sample
genome_counts_row <- genome_counts %>%
  mutate_at(vars(-genome),~./sum(.)) %>% 
  column_to_rownames(., "genome") 

GIFTs_elements_community <- to.community(GIFTs_elements_filtered,genome_counts_row,GIFT_db)
GIFTs_functions_community <- to.community(GIFTs_functions,genome_counts_row,GIFT_db)
GIFTs_domains_community <- to.community(GIFTs_domains,genome_counts_row,GIFT_db)

6.2 Community elements differences

element_gift <- GIFTs_elements_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "sample") %>% 
  merge(., sample_metadata[c(1,4)], by="sample")

uniqueGIFT_db<- unique(GIFT_db[c(2,4,5,6)]) %>% unite("Function",Function:Element, sep= "_", remove=FALSE)

significant_elements <- element_gift %>%
    pivot_longer(-c(sample,region), names_to = "trait", values_to = "value") %>%
    group_by(trait) %>%
    summarise(p_value = wilcox.test(value ~ region, exact = FALSE)$p.value) %>%
    mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
    filter(p_adjust < 0.05)%>%
  rownames_to_column(., "Elements")  %>%
  dplyr::left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))

element_gift_t <- element_gift  %>% 
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "trait")

element_gift_filt <- subset(element_gift_t, trait %in% significant_elements$trait) %>% 
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "sample")%>% 
  dplyr::left_join(., sample_metadata[c(1,4)], by = join_by(sample == sample))


element_gift_filt_means <- element_gift_filt %>%
  dplyr::select(-sample)%>%
  group_by(region)  %>%
  summarise(across(everything(), mean))%>%
   t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Elements")  %>%
  dplyr::left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))

element_gift_filt_sd <- element_gift_filt %>%
  dplyr::select(-sample)%>%
  group_by(region)  %>%
  summarise(across(everything(), sd))%>%
   t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Elements")  %>%
  dplyr::left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))

only three plots are shown to illustrate the plotting function.

6.3 Community functions differences

Code_function	Daneborg	Ittoqqortoormiit	Function
B02	0.367	0.338	Amino acid biosynthesis
B03	0.099	0.094	Amino acid derivative biosynthesis
B04	0.479	0.506	SCFA biosynthesis
B09	0.005	0.003	Metallophore biosynthesis
D01	0.044	0.056	Lipid degradation
D02	0.204	0.223	Polysaccharide degradation
D03	0.210	0.179	Sugar degradation
D06	0.126	0.106	Nitrogen compound degradation
D07	0.288	0.332	Alcohol degradation
D09	0.142	0.163	Antibiotic degradation
S01	0.412	0.404	Cellular structure
S03	0.223	0.272	Spore

only 3 plots are shown to illustrate the plotting function.

6.4 Community domains differences

No differences

6.5 Functional capacity of the MAGs

element
function

7 Differential abundance analysis

7.1 Remove structural zeros

# remove MAGs only present in one region
Daneborg_samples <- sample_metadata %>% 
                    filter(region == "Daneborg") %>% 
                    dplyr::select(sample) %>% pull()

Ittoqqortoormiit_samples <- sample_metadata %>% 
                    filter(region == "Ittoqqortoormiit") %>% 
                    dplyr::select(sample) %>% pull()

structural_zeros <- genome_counts %>% 
   dplyr::rowwise() %>% #compute for each row (genome)
   mutate(all_zeros_Ittoqqortoormiit = all(c_across(all_of(Ittoqqortoormiit_samples)) == 0)) %>% # set true if all samples in Ittoqqortoormiit have zeros
   mutate(all_zeros_Daneborg = all(c_across(all_of(Daneborg_samples)) == 0)) %>% # set true if all samples in Daneborg have zeros
   mutate(average_Ittoqqortoormiit = mean(c_across(all_of(Ittoqqortoormiit_samples)), na.rm = TRUE)) %>% # get average genome counts across Ittoqqortoormiit
   mutate(average_Daneborg = mean(c_across(all_of(Daneborg_samples)), na.rm = TRUE)) %>% # get average genome counts across Daneborg
   filter(all_zeros_Ittoqqortoormiit == TRUE || all_zeros_Daneborg==TRUE)  %>% # filter only genomes with structural zeros
   mutate(present = case_when(
      all_zeros_Ittoqqortoormiit & !all_zeros_Daneborg ~ "Daneborg",
      !all_zeros_Ittoqqortoormiit & all_zeros_Daneborg ~ "Ittoqqortoormiit",
      !all_zeros_Ittoqqortoormiit & !all_zeros_Daneborg ~ "None",
      TRUE ~ NA_character_
    )) %>%
   mutate(average = ifelse(present == "Ittoqqortoormiit", average_Ittoqqortoormiit, average_Daneborg)) %>%
   dplyr::select(genome, present, average) %>%
   dplyr::left_join(genome_metadata, by=join_by(genome==genome)) %>%
   arrange(present,-average)

Create phyloseq object (without structural zeros)

#phyloseq object without structural zeros

# OTU table
phylo_counts <- genome_counts %>% # genome size normalized counts
  dplyr::filter(!genome %in% structural_zeros$genome) %>% # remove structural zeros
  column_to_rownames("genome") %>%
  mutate_all(~ replace(., . == 0, 0.00001)) %>%
  otu_table(., taxa_are_rows = TRUE)

phylo_samples <- sample_metadata %>% 
                    column_to_rownames("sample") %>% 
                    sample_data() #convert to phyloseq sample_data object

# Tax table
phylo_taxonomy <- genome_metadata %>% 
                    filter(genome %in% rownames(phylo_counts)) %>% # remove structural zeros
                    mutate(genome2=genome) %>% #create a pseudo genome name column
                    column_to_rownames("genome2") %>% 
                    dplyr::select(domain,phylum,class,order,family,genus,species,genome) %>% #add an additional taxonomic level to ensure genome-level analysis (as no all genomes have species-level taxonomic assignments. Otherwise, ANCOMBC2 aggregates analyses per species)
                    as.matrix() %>% 
                    tax_table() #convert to phyloseq tax_table object 

# Tree
tree <- phytools::force.ultrametric(tree, method = "extend")

## ***************************************************************
## *                          Note:                              *
## *    force.ultrametric does not include a formal method to    *
## *    ultrametricize a tree & should only be used to coerce    *
## *   a phylogeny that fails is.ultrametric due to rounding --  *
## *    not as a substitute for formal rate-smoothing methods.   *
## ***************************************************************

phylo_tree = phyloseq::phy_tree(tree)

#Generate phyloseq object required to input ANCOMBC
genome_data <- phyloseq(phylo_counts, phylo_taxonomy, phylo_samples, phylo_tree)

#clr transformation
physeq_clr <- microbiome::transform(genome_data, 'clr')

count_crl <- data.frame(physeq_clr@otu_table)
count_crl_t <- data.frame(t(count_crl))
metadata_crl <- data.frame(physeq_clr@sam_data)
metadata_crl <- rownames_to_column(metadata_crl, "sample")
metadata_crl <- metadata_crl[match(rownames(count_crl_t),metadata_crl$sample),]
design <- metadata_crl[, c("sample", "region")]

7.1.2 MAGs in different between location and shared between locations

## Upset visualization: MAGs in different locations and shared among locations
library(UpSetR)
locationcolors=c('#c4d7d1','#408892')

genome_counts_row<-genome_counts %>%column_to_rownames(., "genome")
Ittoqqortoormiit <- genome_counts_row[, colnames(genome_counts_row) %in% Ittoqqortoormiit_samples]%>%
  rowSums()%>% as.data.frame()%>% dplyr::rename(Ittoqqortoormiit=".")%>%rownames_to_column(., "genome")
  
table_upset_analysis <- genome_counts_row[, colnames(genome_counts_row) %in% Daneborg_samples]%>%
  rowSums()%>% as.data.frame()%>% dplyr::rename(Daneborg=".")%>%rownames_to_column(., "genome")%>%
      dplyr::left_join(., Ittoqqortoormiit, by = join_by(genome == genome))%>%column_to_rownames(., "genome")

table_upset_analysis_binary <- ifelse(table_upset_analysis > 0, 1, 0) %>% as.data.frame()

#pdf("figures/MAG_intersection.pdf",width=8,height=6, onefile=F)
upset(as.data.frame(table_upset_analysis_binary),
  keep.order = T,
#  sets = rev(c("feces","cloaca")),
  sets.bar.color= rev(locationcolors),
  mb.ratio = c(0.55, 0.45), order.by = "freq")

#dev.off()

Total number only present in Ittoqqortoormiit and absent in Daneborg

[1] 88

Mags only present in Ittoqqortoormiit with species-level annotation

no_daneborg%>%
    filter(species != "s__") # 45 Ittoqqortoormiit unique MAGs WITH species-level annotation

Number of MAGs present only in Ittoqqortoormiit without species-level annotation

new_species_absent <- no_daneborg %>%
    filter(species == "s__") %>%
    nrow()
no_daneborg %>%
    filter(species == "s__")%>% dplyr::select(c(1:6))
new_species_absent*100/total_absent # 43 Ittoqqortoormiit unique MAGs w/o species-level annotation

Total number only present in Daneborg and absent in Ittoqqortoormiit

Mags only present in Daneborg with species-level annotation

no_Ittoqqortoormiit%>%
    filter(species != "s__") # 16 Daneborg unique MAGs WITH species-level annotation

Number of MAGs present only in Daneborg without species-level annotation

new_species_Ittoqqortoormiit <- no_Ittoqqortoormiit %>%
    filter(species == "s__") %>%
    nrow()
no_Ittoqqortoormiit %>%
    filter(species == "s__") %>% dplyr::select(c(1:6))
cat(new_species_Ittoqqortoormiit*100/total_absent_Ittoqqortoormiit) # 21 Daneborg unique MAGs w/o species-level annotation

7.2 Wilcoxon testing / (Mann Whitney U)

Daneborg phylum summary

              Phylum        Mean          SD
   p__Fusobacteriota 40.23608681  8.74279095
     p__Bacteroidota 33.98263660 10.13627575
      p__Bacillota_A 11.41121322  5.63703542
   p__Pseudomonadota  9.97027845  3.43239492
        p__Bacillota  1.78251891  1.07519999
      p__Bacillota_C  1.69036077  0.77215177
 p__Deferribacterota  0.45273719  0.48325860
 p__Campylobacterota  0.31755732  0.58481784
   p__Actinomycetota  0.07535572  0.10221945
      p__Bacillota_B  0.05231129  0.10290571
    p__Spirochaetota  0.02894372  0.06589219

Ittoqqortoormiit phylum summary

              Phylum       Mean         SD
   p__Fusobacteriota 37.9104994 18.5204556
     p__Bacteroidota 22.6181011 12.0215400
      p__Bacillota_A 19.5577191  9.6774519
   p__Pseudomonadota 11.6158875  8.8900704
        p__Bacillota  4.1347908  5.2284457
      p__Bacillota_C  1.7522190  0.7999087
 p__Campylobacterota  1.2864882  1.7873106
 p__Deferribacterota  0.4369387  0.8013901
      p__Bacillota_B  0.3030163  0.6242852
   p__Actinomycetota  0.2918390  0.3103995
    p__Spirochaetota  0.0925010  0.2807852

Genome level
both regional analyses are done in the same manner, thus only Ittoqqortoormiit is shown.
Ittoqqortoormiit

physeq_ittoq <- subset_samples(physeq_clr, region == "Ittoqqortoormiit")
physeq_ittoq <- prune_taxa(taxa_sums(physeq_ittoq)>0, physeq_ittoq)
table.rel1_ittoq <- physeq_ittoq@otu_table
means.table.rel1_ittoq <- as.data.frame(rowMeans(table.rel1_ittoq))
sd.table.rel1_ittoq <- as.data.frame(matrixStats::rowSds(table.rel1_ittoq, useNames = TRUE))
summary.ittoq <- merge(means.table.rel1_ittoq, sd.table.rel1_ittoq, by="row.names")
colnames(summary.ittoq) <- c("Genome","Mean", "SD")
head(summary.ittoq[order(-summary.ittoq$Mean),], row.names = FALSE)

             Genome     Mean       SD
153  EHA01921_bin.7 8.339900 1.024674
143 EHA01918_bin.24 8.219120 1.310063
17   EHA01876_bin.7 8.198880 1.187592
106 EHA01907_bin.13 8.060607 1.352825
96  EHA01904_bin.12 7.949196 1.252277
164 EHA01926_bin.12 7.943538 1.364449

Daneborg

            Genome     Mean       SD
25  EHA01876_bin.8 9.135991 2.669166
1  EHA01871_bin.13 8.508479 2.540384
33 EHA01878_bin.29 8.305711 2.520488
59 EHA01886_bin.14 8.008138 1.588673
94 EHA01893_bin.29 7.954409 1.188865
24  EHA01876_bin.7 7.860446 2.618191

Genome

clr_t <- as.data.frame(t(as.matrix(physeq_clr@otu_table))) %>%
  rownames_to_column(., "sample")%>% 
  dplyr::left_join(., sample_metadata[c(1,4)], by = join_by(sample == sample))
significant_genome <- clr_t %>%
    pivot_longer(-c(sample,region), names_to = "Genome", values_to = "value") %>%
    group_by(Genome) %>%
    summarise(p_value = wilcox.test(value ~ region, exact = FALSE)$p.value) %>%
    mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
    filter(p_adjust < 0.05)
significant_genome

# A tibble: 145 × 3
   Genome           p_value    p_adjust
   <chr>              <dbl>       <dbl>
 1 EHA01871_bin.13 1.65e- 5 0.000145   
 2 EHA01871_bin.23 2.42e- 4 0.00149    
 3 EHA01871_bin.4  7.42e- 8 0.00000354 
 4 EHA01872_bin.30 1.59e- 2 0.0472     
 5 EHA01872_bin.35 2.68e- 5 0.000222   
 6 EHA01872_bin.38 7.39e- 4 0.00378    
 7 EHA01872_bin.6  8.94e-10 0.000000379
 8 EHA01873_bin.16 1.89e- 7 0.00000478 
 9 EHA01875_bin.14 9.40e- 3 0.0302     
10 EHA01875_bin.22 1.23e- 2 0.0377     
# ℹ 135 more rows

physeq_genome_filtered_rel <-  microbiome::transform(genome_data, "compositional")
genome <- as.data.frame(t(as.matrix(physeq_genome_filtered_rel@otu_table)))
significant_genome_table <- genome[, colnames(genome) %in% significant_genome$Genome] %>%
  rownames_to_column(., "sample")%>%  
  dplyr::left_join(., sample_metadata[c(1,4)], by = join_by(sample == sample))%>%
  dplyr::select(-sample)

colNames <- names(significant_genome_table)[1:3] # change to change number of plotted MAGs
for(i in colNames){
  plt <- ggplot(significant_genome_table, aes(x=region, y=.data[[i]], color = region)) +
    geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
  geom_jitter(width = 0.1, show.legend = TRUE) +
  theme_minimal() +
  theme(
    axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank())
print(plt)
}

only three plots are shown to illustrate the plotting function

Genera level

# A tibble: 26 × 3
   Genus                                                        p_value p_adjust
   <chr>                                                          <dbl>    <dbl>
 1 d__Bacteria_p__Deferribacterota_c__Deferribacteres_o__Defer… 2.55e-3  1.82e-2
 2 d__Bacteria_p__Fusobacteriota_c__Fusobacteriia_o__Fusobacte… 6.20e-3  3.16e-2
 3 g__Allobaculum                                               7.95e-9  4.25e-7
 4 g__Anaerobiospirillum                                        7.48e-3  3.48e-2
 5 g__Avimicrobium                                              1.43e-5  3.07e-4
 6 g__Bacteroides                                               1.08e-2  4.80e-2
 7 g__Blautia_A                                                 7.51e-6  2.01e-4
 8 g__CAJMNU01                                                  3.92e-4  3.81e-3
 9 g__CALUXS01                                                  3.69e-4  3.81e-3
10 g__Campylobacter_D                                           5.64e-3  3.02e-2
# ℹ 16 more rows

only three plots shown to illustrate the plotting function.

7.3 RDA

Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999

Model: rda(formula = count_crl_t ~ region, data = design)
         Df Variance     F Pr(>F)    
Model     1   1420.6 9.205  0.001 ***
Residual 56   8642.3                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999

Model: rda(formula = hel1 ~ region, data = design)
         Df Variance      F Pr(>F)    
Model     1  0.08720 15.614  0.001 ***
Residual 56  0.31275                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

$r.squared
[1] 0.1411698

$adj.r.squared
[1] 0.1258336

$r.squared
[1] 0.2180283

$adj.r.squared
[1] 0.2040645

Permutation test for rda under reduced model
Permutation: free
Number of permutations: 999

Model: rda(formula = count_crl_t ~ region, data = design)
         Df Variance     F Pr(>F)    
Model     1   1420.6 9.205  0.001 ***
Residual 56   8642.3                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Permutation test for rda under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 999

Model: rda(formula = count_crl_t ~ region, data = design)
         Df Variance     F Pr(>F)    
region    1   1420.6 9.205  0.001 ***
Residual 56   8642.3                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Permutation test for rda under reduced model
Forward tests for axes
Permutation: free
Number of permutations: 999

Model: rda(formula = count_crl_t ~ region, data = design)
         Df Variance     F Pr(>F)    
RDA1      1   1420.6 9.205  0.001 ***
Residual 56   8642.3                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

7.4 Enrichment analysis between location

7.4.1 Edger

       regionIttoqqortoormiit
Down                      124
NotSig                    178
Up                        253

genome genera

7.5 DESeq2

Genome

diagdds = phyloseq_to_deseq2(genome_data, ~ region)

diagdds <- estimateSizeFactors(diagdds, type="poscounts",locfunc=genefilter::shorth)
diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
diagdds.ins.fis <- results(diagdds, alpha=0.01, contrast=c("region", "Ittoqqortoormiit", "Daneborg"))
sigtab_diagdds.ins.fis <- diagdds.ins.fis[which(diagdds.ins.fis$pvalue < 0.05), ]
sigtab_diagdds_with_tax <- cbind(as(sigtab_diagdds.ins.fis, "data.frame"), as(tax_table(genome_data)[row.names(sigtab_diagdds.ins.fis), ], "matrix"))
#sigtab_diagdds_with_tax[order(sigtab_diagdds_with_tax$baseMean, decreasing=T), ]
deseq2_group <- as.data.frame(sigtab_diagdds_with_tax)
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
  scale_fill_brewer(palette = palname, ...)
}

x = tapply(sigtab_diagdds_with_tax$log2FoldChange, sigtab_diagdds_with_tax$order, function(x) max(x))
x = sort(x, TRUE)
sigtab_diagdds_with_tax$Family = factor(as.character(sigtab_diagdds_with_tax$order), levels=names(x))
x = tapply(sigtab_diagdds_with_tax$log2FoldChange, sigtab_diagdds_with_tax$genus, function(x) max(x))
x = sort(x, TRUE)
sigtab_diagdds_with_tax$Genus = factor(as.character(sigtab_diagdds_with_tax$genus), levels=names(x))

Genera

## 7.6 ANCOM-BC

phylo_counts_ancom <- genome_counts %>% 
                    filter(!genome %in% structural_zeros$genome) %>% # remove structural zeros
                    column_to_rownames("genome") %>% 
                    mutate_all(~ replace(., . == 0, 0.00001)) %>% #add pseudo counts to avoid structural zero issues 
                    otu_table(., taxa_are_rows = TRUE)

#Generate phyloseq object required to input ANCOMBC, only difference is the addition of the psudocounts
physeq_ancom <- phyloseq(phylo_counts_ancom, phylo_taxonomy, phylo_samples)

Genome

library(ANCOMBC)
set.seed(1234) #set seed for reproducibility
ancom_rand_output = ancombc2(data = physeq_ancom, 
                  assay_name = NULL,
                  tax_level = NULL, #change to agglomerate analysis to a higher taxonomic range
                  fix_formula = "region", #fixed variable(s)
#                  rand_formula = "(1|Individual)",
                  p_adj_method = "holm", 
                  pseudo_sens = TRUE,
                  prv_cut = 0.10, 
                  s0_perc = 0.05,
                  group = NULL, 
                  struc_zero = FALSE, 
                  neg_lb = FALSE,
                  alpha = 0.05, 
                  n_cl = 2, 
                  verbose = TRUE,
                  global = FALSE, 
                  pairwise = FALSE, 
                  dunnet = FALSE, 
                  trend = FALSE,
                  iter_control = list(tol = 1e-5, max_iter = 20, verbose = FALSE),
                  em_control = list(tol = 1e-5, max_iter = 100),
                  lme_control = NULL,
                  mdfdr_control = list(fwer_ctrl_method = "holm", B = 100), 
                  trend_control = NULL)

tax <- data.frame(physeq_ancom@tax_table) %>%
  rownames_to_column(., "taxon")

ancombc_rand_table <- ancom_rand_output$res %>%
  dplyr::select(taxon, lfc_regionIttoqqortoormiit, p_regionIttoqqortoormiit) %>%
  filter(p_regionIttoqqortoormiit < 0.05) %>%
  dplyr::arrange(p_regionIttoqqortoormiit) %>%
  merge(., tax, by="taxon")

colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
#  mutate_at(vars(phylum), ~ str_replace(., "[dpcofgs]__", ""))  %>%
  dplyr::right_join(tax, by=join_by(phylum == phylum)) %>%
  dplyr::select(phylum, colors) %>%
  mutate(colors = str_c(colors, "80"))  %>% #add 80% alpha
    unique() %>%
    dplyr::arrange(phylum)

tax_table <- as.data.frame(unique(ancombc_rand_table$phylum))
colnames(tax_table)[1] <- "phylum"
tax_color <- merge(tax_table, colors_alphabetic, by="phylum")%>%
    dplyr::arrange(phylum) %>%
    dplyr::select(colors) %>%
    pull()

Genera

7.7 LinDA

library(mia)
tse_LinDA = mia::makeTreeSummarizedExperimentFromPhyloseq(genome_data) # create tse object from the phyloseq object with structural zeros removed, no pseudo counts. 
tse_LinDA <- mia::subsetByPrevalentTaxa(tse_LinDA, detection = 0, prevalence = 0.1) # filter tse object

Genome

otu.tab <- as.data.frame(assay(tse_LinDA))
meta <- as.data.frame(colData(tse_LinDA)) %>% 
  dplyr::select(region)
LinDA <- LinDA::linda(
  otu.tab, 
  meta, 
  formula = '~region', 
  alpha = 0.01, 
  prev.cut = 0, # we already filtered  
  winsor.quan = 0.97)

# LinDA::linda.plot(LinDA, c('region'))

Genera

## 7.8 ALDEx2

library(ALDEx2)
genome_counts_row <- column_to_rownames(genome_counts, "genome")
genome_counts_row_new <- genome_counts_row[,order(colnames(genome_counts_row))]
#sample_metadata_test <- sample_metadata[order(sample_metadata$sample %in% colnames(genome_counts_row),)]
sample_metadata_new <- sample_metadata[ order(sample_metadata$sample),]
colnames(genome_counts_row_new) %in% sample_metadata_new$sample
dim(sample_metadata_new)[1]==dim(genome_counts_row_new)[2]

genome_counts_aldex <- genome_counts_row_new %>% rownames_to_column(., "genome") %>%
  filter(!genome %in% structural_zeros$genome) %>% # remove structural zeros
  column_to_rownames(var="genome") %>%
  mutate_all(~ . * 1e6) %>% #multiple by a million
  round(0) #round to integer

genome_counts_filtered.clr <- aldex.clr(genome_counts_aldex, 
               sample_metadata_new$region, #not a factor
               mc.samples=128, 
               denom="all", 
               verbose=F)

genome_counts_filtered.ttest <- aldex.ttest(genome_counts_filtered.clr, 
                hist.plot=F, 
                paired.test=F, 
                verbose=F)

genome_counts_filtered.effect <- aldex.effect(genome_counts_filtered.clr, 
              CI=T, 
              verbose=F, 
              include.sample.summary=F, 
              glm.conds=NULL, 
              useMC=F)

genome_counts_filtered.all <- data.frame(genome_counts_filtered.ttest,genome_counts_filtered.effect) %>%
    rownames_to_column(var="genome") %>%
    dplyr::left_join(genome_metadata,by=join_by(genome==genome))

Significance based on posterior probabilities

7.9 Merge Differential abundance results

Upset Analysis

library(UpSetR)

table_upset_DA <- DA_results  %>%
  dplyr::select(., genome, padj_WILCOX, padj_DESEQ, padj_ANCOMBC, padj_LINDA, padj_ALDEX) %>%
  dplyr::rename(
    Wilcoxon = padj_WILCOX,
    DESeq2 = padj_DESEQ, 
    ANCOMBC = padj_ANCOMBC,
    LinDA = padj_LINDA,
    ALDEx = padj_ALDEX
   ) %>%
  column_to_rownames(var="genome")  %>%
  {.[is.na(.)] <- 0; .}

table_upset_DA_binary <- ifelse(table_upset_DA > 0, 1, 0) %>% as.data.frame()
  
methodcolors=c('#c4d7d1','#408892','#c04062','#6b3a59','#e08683')

#pdf("figures/MAG_intersection.pdf",width=8,height=6, onefile=F)
upset(table_upset_DA_binary,
      sets = rev(c("Wilcoxon","DESeq2","ANCOMBC","LinDA","ALDEx")),
      sets.bar.color= rev(methodcolors),
      mb.ratio = c(0.55, 0.45), order.by = "freq")

#dev.off()

# Filtered DA Results (only MAGs found to be DA by 2+ methods)

filtered_DA_results <- DA_results %>%
  dplyr::select(., genome, padj_WILCOX, padj_DESEQ, padj_ANCOMBC, padj_LINDA, padj_ALDEX) %>%
  column_to_rownames(var="genome")  %>%
  {.[is.na(.)] <- 0; .}
filtered_DA_results <- filtered_DA_results %>%
  filter(rowSums(. > 0) >= 2) 

# Merge annotations
filtered_DA_results <- filtered_DA_results %>%
  rownames_to_column(var="genome")

filtered_DA_results <- subset(DA_results, genome %in% filtered_DA_results$genome)

filtered_DA_results <- filtered_DA_results %>%
  mutate(enrichment = ifelse(LogFold_ANCOMBC > 0 | LogFold_LINDA > 0 | LogFold_DESEQ > 0, "Ittoqqortoormiit",
                      ifelse(LogFold_LINDA < 0 | LogFold_DESEQ < 0, "Daneborg", NA)))

#library("writexl")
#write_xlsx(filtered_DA_results, "/Users/elsa/Desktop/THESIS/sled_dog_metagenomics\\DA_merged.xlsx")

Mags from DA with species-level annotation

filtered_DA_results%>%
    filter(species != "s__") # 109 DA MAGs WITH species-level annotation therefore 77 DA MAGs are w/o species level annotation

8 Kraken

summary data

Mean for Cetacea_. :
# A tibble: 2 × 2
  region          mean_Cetacea_.
  <chr>                    <dbl>
1 Daneborg                 0.883
2 Ittoqqortoormii          0.897
Mean for Pinnipedia_. :
# A tibble: 2 × 2
  region          mean_Pinnipedia_.
  <chr>                       <dbl>
1 Daneborg                    0.497
2 Ittoqqortoormii             0.490
Mean for Eukaryota_. :
# A tibble: 2 × 2
  region          mean_Eukaryota_.
  <chr>                      <dbl>
1 Daneborg                    8.86
2 Ittoqqortoormii             8.17
Mean for Cryptosporidium_. :
# A tibble: 2 × 2
  region          mean_Cryptosporidium_.
  <chr>                            <dbl>
1 Daneborg                       0.00576
2 Ittoqqortoormii                0.00762
Mean for Giardia_. :
# A tibble: 2 × 2
  region          mean_Giardia_.
  <chr>                    <dbl>
1 Daneborg              0.000362
2 Ittoqqortoormii       0.000506
Mean for Sarcocystis_. :
# A tibble: 2 × 2
  region          mean_Sarcocystis_.
  <chr>                        <dbl>
1 Daneborg                  0.00002 
2 Ittoqqortoormii           0.000247
Mean for Toxoplasma_gondii_. :
# A tibble: 2 × 2
  region          mean_Toxoplasma_gondii_.
  <chr>                              <dbl>
1 Daneborg                         0.00445
2 Ittoqqortoormii                  0.00534
Mean for Trichinella_. :
# A tibble: 2 × 2
  region          mean_Trichinella_.
  <chr>                        <dbl>
1 Daneborg                   0.0103 
2 Ittoqqortoormii            0.00831
Mean for Uncinaria_. :
# A tibble: 2 × 2
  region          mean_Uncinaria_.
  <chr>                      <dbl>
1 Daneborg                0.00122 
2 Ittoqqortoormii         0.000072
Mean for Echinococcus_. :
# A tibble: 2 × 2
  region          mean_Echinococcus_.
  <chr>                         <dbl>
1 Daneborg                     0.0129
2 Ittoqqortoormii              0.0104
Mean for Taenia_. :
# A tibble: 2 × 2
  region          mean_Taenia_.
  <chr>                   <dbl>
1 Daneborg              0.00106
2 Ittoqqortoormii       0.00170
Mean for Toxascaris_. :
# A tibble: 2 × 2
  region          mean_Toxascaris_.
  <chr>                       <dbl>
1 Daneborg                0.0000233
2 Ittoqqortoormii         0.000877 
Mean for Cestoda_. :
# A tibble: 2 × 2
  region          mean_Cestoda_.
  <chr>                    <dbl>
1 Daneborg                0.128 
2 Ittoqqortoormii         0.0979
Mean for Nematoda_. :
# A tibble: 2 × 2
  region          mean_Nematoda_.
  <chr>                     <dbl>
1 Daneborg                  0.249
2 Ittoqqortoormii           0.269
Mean for Virus_. :
# A tibble: 2 × 2
  region          mean_Virus_.
  <chr>                  <dbl>
1 Daneborg              0.0731
2 Ittoqqortoormii       0.377 
Mean for PARVO_Parvoviridae_. :
# A tibble: 2 × 2
  region          mean_PARVO_Parvoviridae_.
  <chr>                               <dbl>
1 Daneborg                            0.262
2 Ittoqqortoormii                     0.405
Mean for PARVO_Bocaparvovirus_. :
# A tibble: 2 × 2
  region          mean_PARVO_Bocaparvovirus_.
  <chr>                                 <dbl>
1 Daneborg                             0.0563
2 Ittoqqortoormii                      0.392 
Mean for ADENO_Adenoviridae_. :
# A tibble: 2 × 2
  region          mean_ADENO_Adenoviridae_.
  <chr>                               <dbl>
1 Daneborg                           0.0124
2 Ittoqqortoormii                    0.0816
Mean for PAP_Papillomaviridae_. :
# A tibble: 2 × 2
  region          mean_PAP_Papillomaviridae_.
  <chr>                                 <dbl>
1 Daneborg                             0.0262
2 Ittoqqortoormii                      0.211

Standard Deviation for Cetacea_. :
# A tibble: 2 × 2
  region          sd_Cetacea_.
  <chr>                  <dbl>
1 Daneborg              0.0539
2 Ittoqqortoormii       0.0499
Standard Deviation for Pinnipedia_. :
# A tibble: 2 × 2
  region          sd_Pinnipedia_.
  <chr>                     <dbl>
1 Daneborg                 0.0186
2 Ittoqqortoormii          0.0724
Standard Deviation for Eukaryota_. :
# A tibble: 2 × 2
  region          sd_Eukaryota_.
  <chr>                    <dbl>
1 Daneborg                  1.73
2 Ittoqqortoormii           2.21
Standard Deviation for Cryptosporidium_. :
# A tibble: 2 × 2
  region          sd_Cryptosporidium_.
  <chr>                          <dbl>
1 Daneborg                     0.00164
2 Ittoqqortoormii              0.00319
Standard Deviation for Giardia_. :
# A tibble: 2 × 2
  region          sd_Giardia_.
  <chr>                  <dbl>
1 Daneborg            0.000799
2 Ittoqqortoormii     0.000526
Standard Deviation for Sarcocystis_. :
# A tibble: 2 × 2
  region          sd_Sarcocystis_.
  <chr>                      <dbl>
1 Daneborg               NA       
2 Ittoqqortoormii         0.000186
Standard Deviation for Toxoplasma_gondii_. :
# A tibble: 2 × 2
  region          sd_Toxoplasma_gondii_.
  <chr>                            <dbl>
1 Daneborg                       0.00130
2 Ittoqqortoormii                0.00501
Standard Deviation for Trichinella_. :
# A tibble: 2 × 2
  region          sd_Trichinella_.
  <chr>                      <dbl>
1 Daneborg                 0.00283
2 Ittoqqortoormii          0.00165
Standard Deviation for Uncinaria_. :
# A tibble: 2 × 2
  region          sd_Uncinaria_.
  <chr>                    <dbl>
1 Daneborg             0.00469  
2 Ittoqqortoormii      0.0000726
Standard Deviation for Echinococcus_. :
# A tibble: 2 × 2
  region          sd_Echinococcus_.
  <chr>                       <dbl>
1 Daneborg                  0.0164 
2 Ittoqqortoormii           0.00585
Standard Deviation for Taenia_. :
# A tibble: 2 × 2
  region          sd_Taenia_.
  <chr>                 <dbl>
1 Daneborg            0.00214
2 Ittoqqortoormii     0.00286
Standard Deviation for Toxascaris_. :
# A tibble: 2 × 2
  region          sd_Toxascaris_.
  <chr>                     <dbl>
1 Daneborg             0.00000577
2 Ittoqqortoormii      0.000941  
Standard Deviation for Cestoda_. :
# A tibble: 2 × 2
  region          sd_Cestoda_.
  <chr>                  <dbl>
1 Daneborg              0.150 
2 Ittoqqortoormii       0.0405
Standard Deviation for Nematoda_. :
# A tibble: 2 × 2
  region          sd_Nematoda_.
  <chr>                   <dbl>
1 Daneborg               0.0662
2 Ittoqqortoormii        0.0541
Standard Deviation for Virus_. :
# A tibble: 2 × 2
  region          sd_Virus_.
  <chr>                <dbl>
1 Daneborg            0.0331
2 Ittoqqortoormii     0.798 
Standard Deviation for PARVO_Parvoviridae_. :
# A tibble: 2 × 2
  region          sd_PARVO_Parvoviridae_.
  <chr>                             <dbl>
1 Daneborg                          0.147
2 Ittoqqortoormii                   1.92 
Standard Deviation for PARVO_Bocaparvovirus_. :
# A tibble: 2 × 2
  region          sd_PARVO_Bocaparvovirus_.
  <chr>                               <dbl>
1 Daneborg                           0.0426
2 Ittoqqortoormii                    1.92  
Standard Deviation for ADENO_Adenoviridae_. :
# A tibble: 2 × 2
  region          sd_ADENO_Adenoviridae_.
  <chr>                             <dbl>
1 Daneborg                        0.00951
2 Ittoqqortoormii                 0.114  
Standard Deviation for PAP_Papillomaviridae_. :
# A tibble: 2 × 2
  region          sd_PAP_Papillomaviridae_.
  <chr>                               <dbl>
1 Daneborg                           0.0231
2 Ittoqqortoormii                    0.263

Wilcoxon testing (percentages)

wilcox_kraken <- kraken_subset %>%
    pivot_longer(-c(sample, region), names_to = "Parasite", values_to = "value") %>%
    group_by(Parasite) %>%
    summarise(p_value = {
        if (n_distinct(region) >= 2) {
            wilcox.test(value ~ region, data = cur_data(), exact = TRUE)$p.value
        } else {
            NA
        }
    }) %>%
    mutate(p_adjust = ifelse(!is.na(p_value), p.adjust(p_value, method = "BH"), NA))

significant_kraken <- wilcox_kraken %>%
  filter(!is.na(p_adjust) & p_adjust < 0.05) %>%
  knitr::kable()

Fishers Exact testing (presence / absence)


    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.02564066        Inf
sample estimates:
odds ratio 
       Inf

Elsa-Brenner-Masters-Thesis-Analysis-FINAL

2024-03-14

1 Set up working Environment

1.1 Load required libraries and set working directory

2 Data pre-processing

2.1 General statistics

2.2 General MAG statistics

3 MAG catalogue

3.1 MAG tree

3.2 MAG quality

3.3 Sequencing depth assessment

3.4 Estimated vs. mapped prokaryotic fraction

3.5 Additional sequencing needed

3.6 Relative abundance

4 Alpha diversity calculations

4.1 Alpha diversity means by sample type

4.2 Plot alpha diversities

5 Beta diversity

5.1 Neutral beta diversity

5.2 Phylogenetic beta diversity

5.3 Functional beta diversity

6 Functional capacity analysis

6.1 Functional GIFT distillation:

6.2 Community elements differences

6.3 Community functions differences

6.4 Community domains differences

6.5 Functional capacity of the MAGs

7 Differential abundance analysis

7.1 Remove structural zeros

7.1.2 MAGs in different between location and shared between locations

7.2 Wilcoxon testing / (Mann Whitney U)

7.3 RDA

7.4 Enrichment analysis between location

7.4.1 Edger

7.5 DESeq2

7.7 LinDA

7.9 Merge Differential abundance results

8 Kraken